Uncertainty in Artificial Intelligence
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A Framework for Optimizing Paper Matching
Laurent Charlin, Richard Zemel, Craig Boutilier
At the heart of many scientific conferences is the problem of matching submitted papers to suitable reviewers. Arriving at a good assignment is a major and important challenge for any conference organizer. In this paper we propose a framework to optimize paper-to-reviewer assignments. Our framework uses suitability scores to measure pairwise affinity between papers and reviewers. We show how learning can be used to infer suitability scores from a small set of provided scores, thereby reducing the burden on reviewers and organizers. We frame the assignment problem as an integer program and propose several variations for the paper-to-reviewer matching domain. We also explore how learning and matching interact. Experiments on two conference data sets examine the performance of several learning methods as well as the effectiveness of the matching formulations.
Pages: 86-95
PS Link:
PDF Link: /papers/11/p86-charlin.pdf
AUTHOR = "Laurent Charlin and Richard Zemel and Craig Boutilier",
TITLE = "A Framework for Optimizing Paper Matching",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "86--95"

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